Simulation and Analysis of Particle Filter Based Slam System

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The paper describes a problem and an algorithm for simultaneous localization and mapping (SLAM) for an unmanned aerial vehicle (UAV). The algorithm developed by the authors estimates the flight trajectory and builds a map of the terrain below the UAV. As a tool for estimating the UAV position and other parameters of flight, a particle filter was applied. The proposed algorithm was tested and analyzed by simulations and the paper presents a simulator developed by the authors and used for SLAM testing purposes. Chosen simulation results, including maps and UAV trajectories constructed by the SLAM algorithm are included in the paper.

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  • [1] Bailey T. Durrant-Whyte H.: Simultaneous localization and mapping (SLAM): part II. IEEE Robotics & Automation Magazine Vol. 13 Issue 3 2006 pp. 108-117.

  • [2] Baya H. Essa A. Tuytelaarsb T. Van Goolab L. (2008) Speeded-Up Robust Features (SURF). Computer Vision and Image Understanding Vol. 110 Issue 3 2008 pp. 346-359.

  • [3] Cadena C. Carlone L. Carrillo H. Latif Y. Scaramuzza D. Neira J. Reid I. Leonard J.: Past Present and Future of Simultaneous Localization and Mapping: Towards the Robust-Perception Age. IEEE Transactions on Robotics Vol. 32 issue 6 2016.

  • [4] Durrant-Whyte H. Leonard J. Cox J.I.: Dynamic map building for autonomous mobile robot. Intelligent Robots and Systems ‘90 1990.

  • [5] Endres F. Hess J. Sturm J. Cremers D. Burgard W.: 3D Mapping with an RGB-D Camera. IEEE Transactions on robotics Vol. 30 issue 1 2010 pp. 177-187.

  • [6] Grisetti G. Kummerle R. Stachniss C. Burgard W.: A Tutorial on Graph-Based SLAM. IEEE Intelligent Transportation Systems Magazine Vol. 2 issue 4 2010 pp. 31-43.

  • [7] Grisetti G. Stachniss C. Burgard W.: Improving Grid-based SLAM with Rao-Blackwellized Particle Filters by Adaptive Proposals and Selective Resampling. Proceedings of the 2005 IEEE International Conference on Robotics and Automation Barcelona Spain 2005 pp. 2432-2437.

  • [8] Hartmann J. Klussendorff J. Maehle E.: A comparison of feature descriptors for visual SLAM European Conference on Mobile Robots 2013.

  • [9] Howard A.: Multi-robot Simultaneous Localization and Mapping using Particle Filters. Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

  • [10] Leonard J.J. Durrant-Whyte H.F.: Directed Sonar Sensing for Mobile Robot Navigation. Kluwer Academic Publishers 1992.

  • [11] Łabowski M. P. Kaniewski P. P. Serafin P.: Inertial navigation system for radar terrain imaging. IEEE/ION Position Location and Navigation Symposium 2016 pp.942-948.

  • [12] Konatowski S. Kaniewski P. Matuszewski J.: Comparison of Estimation Accuracy of EKF UKF and PF Filters. Annual of Navigation 23/2016 pp. 69-87.

  • [13] Santana A.M. Aires K. Veras R. Medeiros A.: An Approach for 2D Visual Occupancy Grid Map Using Monocular Vision. Electronic Notes in Theoretical Computer Science 281 2014 pp. 175-191.

  • [14] Sola J. Marquez D. Codol J. Vidal-Calleja T.: An EKF-SLAM toolbox for MATLAB. 2009.

  • [15] Thrun S. Burgard W. Fox D.: Probabilistic Robotics. MIT Press 2005.

  • [16] Thrun S. Montemerlo M. Koller D. Wegbreit B.: FastSLAM: An Efficient Solution to the Simultaneous Localization and Mapping Problem with Unknown Data Association. Proceedings of the AAAI-02 Conference on Artificial Intelligence 2002 pp. 593-598.

  • [17] Vincent P. Rubin I.: A framework and analysis for cooperative search using UAV swarms. Proceedings of the 2004 ACM symposium on Applied computing pp. 79-86.

  • [18] H. Wang H. F. Guixia F. L. Juan L. Y. Zheping Y. B. Xinqian B.: An Adaptive UKF Based SLAM Method for Unmanned Underwater Vehicle. Mathematical Problems in Engineering 2013.

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